Xidian University

About: Xidian University is a education organization based out in Xi'an, China. It is known for research contribution in the topics: Antenna (radio) & Synthetic aperture radar. The organization has 32099 authors who have published 38961 publications receiving 431820 citations. The organization is also known as: University of Electronic Science and Technology at Xi'an & Xīān Diànzǐ Kējì Dàxué.

A Novel Dynamic Android Malware Detection System With Ensemble Learning

Abstract: With the popularity of Android smartphones, malicious applications targeted Android platform have explosively increased. Proposing effective Android malware detection method for preventing the spread of malware has become an emerging issue. Various features extracted through static and dynamic analysis in conjunction with machine learning algorithm have been the mainstream in large-scale malware identification. In general, static analysis becomes invalid in detecting applications which adopt sophisticated obfuscation techniques like encryption or dynamic code loading. However, dynamic analysis is suitable to deal with these evasion techniques. In this paper, we propose an effective dynamic analysis framework, called EnDroid, in the aim of implementing highly precise malware detection based on multiple types of dynamic behavior features. These features cover system-level behavior trace and common application-level malicious behaviors like personal information stealing, premium service subscription, and malicious service communication. In addition, EnDroid adopts feature selection algorithm to remove noisy or irrelevant features and extracts critical behavior features. Extracting behavior features through runtime monitor, EnDroid is able to distinguish malicious from benign applications with ensemble learning algorithm. Through experiments, we prove the effectiveness of EnDroid on two datasets. Furthermore, we find Stacking achieves the best classification performance and is promising in Android malware detection.

Brief Paper: Output-feedback adaptive dynamic surface control of stochastic non-linear systems using neural network

Abstract: For the first time, a dynamic surface control approach is proposed for a class of stochastic non-linear systems with the standard output-feedback form using neural network. The proposed approach is a stochastic vision of the existing dynamic surface control approach which can overcome the problem of 'explosion of complexity' in the backstepping design of stochastic systems. Moreover, all unknown system functions are lumped into a suitable unknown function which is compensated for using only a neural network. The proposed control approach is simpler than the existing backstepping control methods for stochastic systems. Two examples are given to illustrate the effectiveness of the proposed design approach.

Multi-focus image fusion based on non-subsampled shearlet transform

Abstract: In this study, a new multi-focus image fusion algorithm based on the non-subsampled shearlet transform (NSST) is presented. First, an initial fused image is acquired by using a conventional multi-resolution image fusion method. The pixels of those source multi-focus images, which have smaller square error with the corresponding pixels of the initial fused image, are considered in the focused regions. Based on this principle, the focused regions are determined, and the morphological opening and closing are employed for post-processing. Then the focused regions and the focused border regions in each source image are identified and used to guide the fusion process in NSST domain. Finally, the fused image is obtained using the inverse NSST. Experimental results show that this proposed method can not only extract more important detailed information from source images, but also avoid the introduction of artificial information effectively. It significantly outperforms the discrete wavelet transform (DWT)-based fusion method, the non-subsampled contourlet-transformbased fusion method and the NSST-based fusion method (see Miao et al. 2011) in terms of both visual quality and objective evaluation.

Frequency-Reconfigurable Bow-Tie Antenna for Bluetooth, WiMAX, and WLAN Applications

Abstract: A frequency-reconfigurable bow-tie antenna for Bluetooth, WiMAX, and WLAN applications is proposed. The bow-tie radiator is printed on two sides of the substrate and is fed by a microstripline continued by a pair of parallel strips. By embedding p-i-n diodes over the bow-tie arms, the effective electrical length of the antenna can be changed, leading to an electrically tunable operating band. The simple biasing circuit used in this design eliminates the need for extra bias lines, and thus avoids distortion of the radiation patterns. Measured results are in good agreement with simulations, which shows that the proposed antenna can be tuned to operate in either 2.2–2.53, 2.97–3.71, or 4.51–6 GHz band with similar radiation patterns.

Deduplication on Encrypted Big Data in Cloud

Abstract: Cloud computing offers a new way of service provision by re-arranging various resources over the Internet. The most important and popular cloud service is data storage. In order to preserve the privacy of data holders, data are often stored in cloud in an encrypted form. However, encrypted data introduce new challenges for cloud data deduplication, which becomes crucial for big data storage and processing in cloud. Traditional deduplication schemes cannot work on encrypted data. Existing solutions of encrypted data deduplication suffer from security weakness. They cannot flexibly support data access control and revocation. Therefore, few of them can be readily deployed in practice. In this paper, we propose a scheme to deduplicate encrypted data stored in cloud based on ownership challenge and proxy re-encryption. It integrates cloud data deduplication with access control. We evaluate its performance based on extensive analysis and computer simulations. The results show the superior efficiency and effectiveness of the scheme for potential practical deployment, especially for big data deduplication in cloud storage.